=Paper=
{{Paper
|id=Vol-1452/paper12
|storemode=property
|title=Studies of Anthropometrical Features using Machine Learning Approach
|pdfUrl=https://ceur-ws.org/Vol-1452/paper12.pdf
|volume=Vol-1452
|dblpUrl=https://dblp.org/rec/conf/aist/NguyenNZ15
}}
==Studies of Anthropometrical Features using Machine Learning Approach==
https://ceur-ws.org/Vol-1452/paper12.pdf
Studies of Anthropometrical Features using
Machine Learning Approach
The Long Nguyen1 , Thu Huong Nguyen1 , and Aleksei Zhukov2
1
Irkutsk State Technical University, Irkutsk, Russia
{thelongit88,thuhuongyb}@gmail.com
2
Irkutsk State University, Irkutsk, Russia
zhukovalex13@gmail.com
Abstract. In this article we propose the novel approach to measure
anthropometrical features such as height, width of shoulder, circumfer-
ence of the chest, hip and waist. The sub-pixel processing and convex
hull technique are used to efficiently measure the features from 2d im-
age. The SVM technique is used to classify men and women based on
measured features. The results of real data processing are presented.
Keywords: anthropometrical features, SVM, image processing, body
size, support vector machine
1 Introduction
Development of efficient methods of image recognition is an important field of
computer sciences. The theory of such methods uses the machine learning meth-
ods enabling an automatic scenes analysis.
Recently, automatic detection and feature extraction of the human bodies is
widely used in many fields such as non-contact measurements of body size [1],
the construction of 3D models of humans [2,3], [12], the analysis of human action
[4] and pose estimation [5].
In this paper we propose a system of non-contact anthropometrical features
extraction based on the analysis of digital images of humans. Background sub-
traction is used to detect contours data. Edge detection operators are employed
for contour detection (silhouette). This approach combines with the algorithm of
face recognition, background subtraction, the detection of skin color and contour
analysis. The convexity hull defects are also used for anthropometrical features
extraction, then the support vector machine (SVM) will be applied for gender
classification.
Cluster analysis methods allow to analyze the space of feature vectors which
obtained sizes to find out the clusters corresponding to the most characteristic
anthropometric features of people, that can help to develop recommendations
for the clothing industry, and also can be used in other fields of natural science,
such as axiology.
The novelty and motivation of our approach is usage of the image process-
ing techniques for efficient measurement of anthropometric features for further
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classification with machine learning techniques. As an application male/female
classification task was examined.
2 Related Work
All detection methods of body parts can be divided into two main categories:
model based and learning methods. Model based approaches are the “top-down”
methods, which are used to obtain preliminary information, e.g. about the shapes
of the human body in various poses [6]. In machine learning, one apply the
principle of learning data to extract useful knowledge from the data.
In [7] an effective approach for silhouette detections is presented. It is used
to represent the contour curves of the form of the human body in the form
of 8-connected chain code Freeman [8]. The classic algorithm for background
subtraction and the Canny edge detector are used to get the silhouette. Then
the contour data is divided into a number of segments. Thus, special rules for
measuring the differences between the directions of the segments are used for
feature extraction points. This approach has several disadvantages including high
sensitivity noise.
The contour of body is also divided into the parts in [9]. The convexity and
curvature boundaries of each segment of the contour are used to define the body
parts.
In [6] the approach for body parts segmentation in noisy silhouette images
was proposed. The weighted radial histogram of distances and directions are
used as features. Authors also used Hidden Markov Model (HMM) to model the
silhouette as a sequence of body parts. A general model is trained using shape
context features extraction from the labeled synthetic data.
In [10] authors use the segmentation method based on sub-pixel data process-
ing and relatively large regions or segments. It also detects a significant upper
and lower extremities from these segments, identifies potential head and torso
positions at the same time. Both modules combine these parts from partial im-
ages configuration of the body, by applying global constraints to recover full
body configurations.
3 Anthropometric Data Extraction
In the first part of the approach we detect the silhouette using the background
subtraction method. The morphological operations including erosion, dilation,
opening, and closing are performed to reduce noise and to smooth the silhouette
contour. A convex hull is created around the silhouette frame, and convexity
defects are used as the features for analysis. Individually these features are the
start and end convexity defect points and convexity defect locations.
To find the convex hull of a 2D set points, we use the Sklansky’s algorithm
[14] that has O(N log N ) complexity.
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Fig. 1. Flowchart of machine learning with anthropological features extraction.
Background Subtraction Algorithm: The method is based on a compar-
ison between the two images, namely foreground (FG) and background images
(BG). The scene of background image is obtained when there is no object mo-
tion [9,10]. Call F G(x, y) is the intensity values of pixel with coordinates (x, y)
in the foreground image, belonging to the interval [0.255]. BG(x, y) is the in-
tensity values of pixel with coordinates (x, y) of the background image. A pixel
with coordinates (x, y) in the foreground image of the dominant component if it
satisfies: |F G(x, y) − BG(x, y)| > T
Where, T (x, y) is the threshold value, which enables initializing by the value
was determined. Pixels with label 1 are an object if |F G(x, y) − BG(x, y)| > T
or not object (value 0) if |F G(x, y) − BG(x, y)| < T .
Sub-pixel Processing: To achieve better accuracy sub-pixel processing we
employed sub-pixel processing. Conventional image processing is performed in
units of 1 pixel, while the sub-pixel processing method performs position detec-
tion in units down to 0.01 pixels. This enables high accuracy position detection,
expanding the application range to precise part location and dimension mea-
surement.
As described in [18] sub-pixel procedure iterates to find the sub-pixel accurate
location of corners, as shown on the fig. 2.
Sub-pixel accurate corner locator is based on the observation that every vec-
tor from the center q to a point p located within a neighborhood of q is orthogonal
98
.
Fig. 2. Sub-pixel corner location principle illustration (Red arrows mean gradient di-
rection).
to the image gradient at p subject to image and measurement noise. Consider
the expression:
i = DIpi T · (q − pi )
where DIpi is an image gradient at one of the points pi in a neighborhood of q.
The value of q is to be found so that i is minimized. A system of equations may
be set up with i set to zero:
X X
(DIpi · DIpi T ) − (DIpi · DIpi T · pi )
i i
where the gradients are summed within a neighborhood of q. Calling the first
gradient term G and the second gradient term b gives:
q = G−1 · b
.
The algorithm sets the center of the neighborhood window at this new center
q and then iterates until the center stays within a set threshold. In our work we
use OpenCV library to perform sub-pixel corner detection [18].
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.
Fig. 3. Sub-pixel processing result.
In our approach sub-pixel helps us to find corners. It uses the dot product
technique to refine corners. The function works iteratively, refining the corners
until the termination criteria is reached. Most sub-pixel algorithms require a
good estimate of the location of the feature. Otherwise, the algorithms may be
attracted to the noise instead of desired features.
Features Extraction based on Convexity Hull Defects:
In our approach the human body is described by convexity defect triangles.
The bodies are represented by triangles with three coordinates called the con-
vexity defect start (xds ; yds ), defect end (xde ; yde ), and defect position points
(xdp ; ydp ), labeled as P1 ,P2 and P3 respectively.
We applied the convex hull method to extract contours and obtained a lot of
convexity defects, including areas with very small depth, even a value of 0 - these
are not areas containing features to be extracted. So, we determined the area of
interest which contains the parts of the body is area that has depth > 50. That
depth value was obtained empirically. Thus we got 5 convex regions correspond
to conditions. Then we determined of the human body, which contains three
parts of chest, waist and hips. We continue applying the convex hull to locate
the waist.
Once we got the coordinates of the points determined, we perform calculation
of the distances between points in pixels, and finally converted the measurements
into cm. The convexity defect of triangle is determined based on: (xds ; yds ),
(xde ; yde ), and (xdp ; ydp ). A convexity defect is presented wherever the contour
of the object is away from the convex hull drawn around the same contour.
Convexity defect gives the set of values for every defect in the form of vector.
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Fig. 4. Flowchart feature extraction based on convex hull.
This vector contains the start and end point of the line of defect in the convex
hull. These points indicate indices of the coordinate points of the contour. These
points can be easily retrieved by using start and end indices of the defect formed
from the contour vector. Convexity defect also includes index of the depth point
in the contour and its depth value from the line. In fact, the person may be
represented by many triangles point defects, it is piecewise convex. However,
in this approach, we are interested in two triangles have the biggest area - It
obviously corresponds to leg-armpit-arm triangle, and it includes the location of
the parts we need to calculate interest: chest, waist, hips.
Finally, we obtain the coordinates of the points on the body.
Therefore in this paper we propose a simpler and cheaper system comparing
with other systems, see e.g. [16]. In our experiments we used single digital camera
and A4 sheet (210 × 297mm) for calibration. Source images for the method must
be captured in special way: with given background and calibration sheet, human
must stand straight with arms stretched.Three dimensions (chest circumference,
waist circumference, hip circumference) were selected because of their relevance
to clothing sizing, and human classification which was the main purpose of our
system. Table 1 shows some results of measurements(from 50 measurements of
people in the database) sizes of human body using convex hull method comparing
with manual method.
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Table 1. Results of measurement sizes of human body using convex hull method.
BODY SIZES Manual method Convex hull method
Chest 87.98 cm 88.12 cm
Waist 67.95 cm 68.05 cm
Hip 90.52 cm 91.68 cm
Chest 88.64 cm 89.02 cm
Waist 66.61 cm 67.13 cm
Hip 93.58 cm 94.93 cm
Chest 87.22 cm 88.15 cm
Waist 67.19 cm 67.96 cm
Hip 89.36 cm 89.01 cm
Chest 88.16 cm 89.46 cm
Waist 65.64 cm 66.42 cm
Hip 92.17 cm 93.02 cm
Chest 90.96 cm 91.23 cm
Waist 71.44 cm 70.82 cm
Hip 93.56 cm 94.19 cm
Chest 86.94 cm 87.12 cm
Waist 67.68 cm 68.12 cm
Hip 85.28 cm 84.56 cm
Measurements errors are mostly caused by camera resolution or non tight
clothing and noises of environment near by the object. In our case, we used a
basis phone camera of model Samsung Galaxy S4 with resolution 13 Mega Pixel.
We recommended using high-quality resolution camera with flash opened during
the time capture photos and people should wear tight clothes body to reduce
maximum noises as measurements errors. In addition, we have also performed
averaging over several measurements to reduce measurement and calibration
errors.
The circumferential measures were generated by approximating the shape of
the respective body part. For example, neck circumference was approximated
with the elliptical shape. The major and minor axes lengths were obtained from
the front and side views. The chest circumference was determined by approx-
imating the shape as a combination of a rectangle and an ellipse, using the
method with formula mentioned in [16].
4 Anthropometric Data Analysis
Base on method anthropometric features extraction which mentioned in previous
section, we collected sizes of human body parts. We have a train set contains
sizes of 50 people sizes (25 men and 25 women) and test set from 18 people (10
men and 8 women). Each measurement contains 3 feature for each object: chest,
waist and hip circumferences.
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4.1 Classification of Men/Women using Support Vector
Machine(SVM)
To solve the problems of classification we used well-known machine learning
method – support vector machine (SVM), proposed by Vladimir Vapnik [11].
We choose SVM as a one of the most effective algorithm which have many real
world applications [17].
Our goal is use a support vector machine for gender classification based on
anthropometric data. As a features we use three human body parameters: chest,
waist, hip circumferences.
The LibSVM library [15] was used as Support Vector Machine implementa-
tion. The following main parameters of SVM were chosen. Radial basis function
with γ = 0.333 parameter was used as a kernel function. SVM model param-
eters Gamma (γ) and cost (C) were obtained empirically. Gamma parameter
is needed for all types of kernels except linear. Constant C is an regularization
term in the Lagrange formulation. We will use the supplied parameter ranges
(C - cost, γ - gamma), using the train set. The range to gamma parameter is
between 0.000001 and 0.1. For cost parameter the range is from 0.1 until 10.
It’s important to understanding the influence of this two parameters, because
the accuracy of an SVM model is largely dependent on the selection them. For
example, if C is too large, we have a high penalty for non separable points and
we may store many support vectors and over-fit. If it is too small, we may have
an under-fitting. The results of this algorithm is shown in Fig.5. Obtained test
classification error for current dataset is 20%.
Fig. 5. The result of classification by SVM. Blue dots: men, red dots: women, green
dots: support vectors.
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5 Conclusion
Human classification is an useful application for many future scenarios of human-
computer interaction. Our approach is presented in this paper describes the
classification of people based on the anthropometrical features using machine
learning approach. Proposed approach allows to extract form images a number
of anthropometrical features, including the length of arms, chest width, shoulder
width, hips, leg length. In this article we propose solution of male/female clas-
sification task by image based on support vector machine. Obtained test error
is sufficiently large but in feature work we are going to examine more classifiers
to choose best one and also use bigger datasets to reduce misclassification rate.
Based on these results, we hope to build solution that can help to solve tasks of
the clothing industry.
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